
Focusing on retrieval, we found that the effectiveness of the sparse retrieval (BM25) baseline is higher than values reported by [1]}.
Whereas they reported that dense–sparse hybrid results do not meaningfully improve over dense retrieval alone, we arrived at the opposite conclusion, where hybrid techniques yield statistically significant gains.
We are able to achieve on average a three-point improvement in top-20 accuracy over the best DPR results across five standard QA test collections.

Focusing on end-to-end QA effectiveness, we explored different techniques for evidence combination to extract the final answer span.
Whereas the original DPR paper only used scores from the reader to identify the final answer span, we investigated combining retriever scores and further experimented with the answer span selection technique described by [2]}.
In our best condition, we were able to achieve statistically significant improvements of around three points on exact match scores over the original DPR implementation, using the same exact models.

